Field of the Invention
The present invention relates to image processing to perform color separation processing and quantization processing.
Description of the Related Art
In an ICC (International Color Consortium) profile that is commonly used in a print device, color material amounts are associated with a device-independent color space, such as L*a*b* and XYZ. As an example of color material amounts, there is a case where color material amounts of 0 to 255 are allocated to color materials, such as cyan (C), magenta (M), yellow (Y), and black (K). Further, there is a printer having more than ten kinds of color material by adding color materials, such as red (R), green (G), and blue (B), in order to reproduce a more vivid color, and adding color materials, such as pale cyan (PC), pale magenta (PM), and gray (Gy), in order to reproduce a multi-tone level.
That is, compared to three-dimensional or four-dimensional data that has been input hitherto commonly as an input signal, such as R, G, and B or C, M, Y, and K, an output of color separation will be data including four to more than ten colors. The fact that the number of dimensions of an output is large such as this is also true with the case where an input is data representing a color in the device-independent color space, such as L*a*b* and XYZ, as in the ICC profile described previously.
In general, in the case where the number of dimensions of an output is large for the number of dimensions of an input, there exist a large number of output solutions. Explanation is given by taking a color separation lookup table (hereinafter, described as “LUT”) as an example. There exists a plurality of combinations of C, M, Y, and K that reproduce a certain one L*a*b*. As to the existence of a plurality of combinations such as this, in the well-known UCR (Under Color Removal) technique, in the case where the color material amounts of C, M, and Y are (C, M, Y)=(70, 50, 30), respectively, then, fromC′=C−Min(C,M,Y)M′=M−Min(C,M,Y)Y′=Y−Min(C,M,Y)K=Min(C,M,Y),(C′, M′, Y′, K)=(40, 20, 0, 30) are calculated. Here, according to the fundamental rule of UCR, color material amount 1 and color material amount 2 expressed as(C1,M1,Y1,K1)=(70,50,30,0)  (color material amount 1)(C2,M2,Y2,K2)=(40,20,0,30)  (color material amount 2)
will reproduce the same color although they are different combinations of color material amounts.
In the above-described example, the case of UCR is explained for the sake of simplification of explanation, but also in GCR (Gray Component Replacement), which is an improved version of UCR, by the setting of a K amount based on a GCR ratio, a plurality of combinations of color material amounts is calculated. Further, this also applies to the case (Japanese Patent No. 4561483) where colors and color material amounts are associated by using the Cellular Yule-Nielsen Spectral Neugebauer Model or the like. That is, a plurality of combinations of color material amounts exists for one color.
The above-described plurality of combinations of color material amounts can reproduce the same color but exhibit different characteristics in various image quality items, such as granularity indicating the roughness of an image, color constancy indicating a change in color under a plurality of observation light sources, a specular gloss unit, and gloss image clarity.
Here, a color separation LUT stores one combination of color material amounts, corresponding to hue values, such as L*a*b* and RGB. Because of this, in the technique of Japanese Patent No. 4561483, by using conditions, such as a color difference from a target color, a color difference under a plurality of observation light sources, granularity, and a color material amount limiting value depending on a printing medium, the solutions are narrowed down to one solution from a plurality of combinations of color material amounts reproducing the same color. That is, what can be stored in the color separation LUT is only one combination among the plurality of combinations of color material amounts capable of reproducing the same color and having different image qualities, such as the granularity and the specular gloss unit.
On the other hand, the subjects of photographs, which are objects to be output by a printer, include a variety of objects, such as highly glossy objects such as metals, lowly glossy objects such as fibers, objects the surface of which is rough, and objects the surface of which is smooth, although they have the same color (hereinafter, the attribute of a subject other than hues, such as gloss and roughness, is described as “texture”).
Further, in the case where printing of an image is performed by a printer, in general, the number of tone levels that can be reproduced per pixel in a printer is small compared to the number of tone levels per pixel in a digital image (e.g., 8 bits/256 tone levels per pixel). Because of this, as a printer in the prior art, one that converts the color separation data indicating color material amounts into quantization data of tone levels less in the number and uses the converted quantization data in area gradation processing is known. As the quantization method such as this, various publicly known techniques exist, such as the error diffusion method and the dither matrix method. It is known that an image to be printed by a printer changes characteristics in image quality to be printed in accordance with the quantization method, such as the error diffusion method and the dither matrix method.